{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,copy,os,inspect\n",
    "if hasattr(sys.modules[__name__], '__file__'):\n",
    "    _file_name = __file__\n",
    "else:\n",
    "    _file_name = inspect.getfile(inspect.currentframe())\n",
    "CURRENT_FILE_PATH = os.path.dirname(_file_name)\n",
    "sys.path.append(os.getcwd()+\"/../neuronVis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import IONData\n",
    "iondata = IONData.IONData()\n",
    "def loadClusterCSV(filename,cluster=None):\n",
    "    neurons = pd.read_csv(filename)\n",
    "    neuronsArray= neurons.to_numpy()\n",
    "    neuronScene=[]\n",
    "    for neuron in neuronsArray:\n",
    "        # print(str(neuron[2])[0])\n",
    "        neurondict={}\n",
    "        neurondict['cluster'] = neuron[1]\n",
    "        neurondict['sampleid'] = str(neuron[2])[0:6]\n",
    "        neurondict['name']=str(neuron[2])[6:]+'.swc'\n",
    "        if cluster ==neuron[1] or cluster ==None:\n",
    "            neuronScene.append(neurondict)\n",
    "    return neuronScene\n",
    "\n",
    "\n",
    "neuronScene = loadClusterCSV(\"../resource/cluster_eachNeuron/cluster_eachNeuron_PB.csv\")\n",
    "\n",
    "leftNeurons=[]\n",
    "rightNeurons=[]\n",
    "for neuron in neuronScene:\n",
    "    property = iondata.getNeuronPropertyByID(neuron['sampleid'], neuron['name'])\n",
    "    somaz = property['somapoint'][2]\n",
    "    if somaz>5700:\n",
    "        neuron['lateral']='right'\n",
    "        rightNeurons.append(neuron)\n",
    "    else:\n",
    "        neuron['lateral']='left'\n",
    "        leftNeurons.append(neuron)\n",
    "    neuron['property']=property\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Visual as nv\n",
    "\n",
    "neuronvis = nv.neuronVis()\n",
    "neuronvis.render.setBackgroundColor((1.0,1.00,1.0,1.0))\n",
    "\n",
    "# for neuron in neuronScene:\n",
    "for neuron in neuronScene:\n",
    "    if neuron['cluster']==8:\n",
    "        neuronT = iondata.getNeuronTreeByID(neuron['sampleid'], neuron['name'])\n",
    "        neuronvis.addNeuronTree(neuronT,'200313-083.swc',somaColor=[0,1,0],axonColor=[1,0,0],dendriteColor=[0,0,1])\n",
    "        # break\n",
    "neuronvis.render.savepng('../resource/pbncluster8.png')\n",
    "neuronvis.render.closeWindow()\n",
    "\n",
    "# neuronvis.render.run()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import seaborn\tas sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.use('module://matplotlib_inline.backend_inline')\n",
    "%matplotlib inline\n",
    "\n",
    "regions=['SI','MSC','VM','VP','SPF','IMD','MD','RE','CM','PCN','CL','PF','LHA','LPO','PSTN','RCH','ZI','SNr','VTA','MRN','SCm','PAG','CUN','PPN','DR','PSV','PB','SOC','PRNc','SUT','P5','LC','PRNr','MY-sen','CU','NTS','VII','GRN','IRN','LRN','MARN','MDRN','PARN','PGRN','MV','ANcr2','COPY','PFL']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import BrainRegion as BR \n",
    "br = BR.BrainRegion()\n",
    "br.praseJson()\n",
    "\n",
    "neuronScenedf = pd.DataFrame(neuronScene)\n",
    "def getRegionProject(Neurons,regions):\n",
    "    clusterProject={}\n",
    "    for neuron in Neurons:\n",
    "        property = iondata.getNeuronPropertyByID(neuron['sampleid'], neuron['name'])\n",
    "        brproperty=BR.RegionProperty(copy.deepcopy(br))\n",
    "        brpropertyLeft=BR.RegionProperty(copy.deepcopy(br))\n",
    "        brpropertyRight=BR.RegionProperty(copy.deepcopy(br))\n",
    "        brproperty.setProperty(property['projectregion'])\n",
    "        brpropertyLeft.setProperty(property['projectleftregion'])\n",
    "        brpropertyRight.setProperty(property['projectrightregion'])\n",
    "\n",
    "        regionProject=[]\n",
    "        for region in regions:\n",
    "                regionsum = brproperty.getSumProperty(region)\n",
    "                regionProject.append(regionsum)\n",
    "        if neuron['cluster'] not in clusterProject.keys():\n",
    "            clusterProject[neuron['cluster']]=np.array(regionProject).astype(np.float64)\n",
    "        else:\n",
    "            clusterProject[neuron['cluster']]+=np.array(regionProject).astype(np.float64)\n",
    "    return clusterProject\n",
    "clusterProjectLeft = getRegionProject(leftNeurons,regions)\n",
    "clusterProjectRight = getRegionProject(rightNeurons,regions)\n",
    "for key in clusterProjectLeft.keys():\n",
    "    clusterProjectLeft[key]/=(neuronScenedf.cluster==key).sum()\n",
    "\n",
    "for key in clusterProjectRight.keys():\n",
    "    clusterProjectRight[key]/=(neuronScenedf.cluster==key).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32md:\\project\\python\\neuron-vis\\figures\\pbnAnalyse.ipynb Cell 6\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/d%3A/project/python/neuron-vis/figures/pbnAnalyse.ipynb#W5sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m fig,ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(figsize\u001b[39m=\u001b[39m(\u001b[39m13\u001b[39m,\u001b[39m13\u001b[39m))\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/python/neuron-vis/figures/pbnAnalyse.ipynb#W5sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m ax0 \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplot(\u001b[39m1\u001b[39m,\u001b[39m2\u001b[39m,\u001b[39m1\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/d%3A/project/python/neuron-vis/figures/pbnAnalyse.ipynb#W5sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m keys\u001b[39m=\u001b[39m\u001b[39mlist\u001b[39m(clusterProjectLeft\u001b[39m.\u001b[39mkeys())\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "fig,ax = plt.subplots(figsize=(13,13))\n",
    "ax0 = plt.subplot(1,2,1)\n",
    "keys=list(clusterProjectLeft.keys())\n",
    "\n",
    "data = []\n",
    "for key in sorted(keys):\n",
    "    data.append(clusterProjectRight[key])\n",
    "mat = np.array(data).T\n",
    "mat = np.log2(mat/1000.0+1)\n",
    "sns.heatmap(mat)\n",
    "plt.xticks([index+0.5 for index in range(len(keys))],tuple(sorted(keys)),fontsize=20)\n",
    "plt.yticks([index+0.5 for index in range(len(regions))],tuple(regions),rotation = 0,fontsize=15)\n",
    "\n",
    "# clusterProjectLeft\n",
    "\n",
    "ax1= plt.subplot(1,2,2)\n",
    "keys=list(clusterProjectRight.keys())\n",
    "print(clusterProjectRight.keys())\n",
    "data = []\n",
    "for key in sorted(keys):\n",
    "    data.append(clusterProjectRight[key])\n",
    "mat = np.array(data).T\n",
    "mat = np.log2(mat/1000.0+1)\n",
    "cg= sns.heatmap(mat)\n",
    "# cg= sns.heatmap(np.array(data).T)\n",
    "# plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), size=20)\n",
    "plt.xticks([index+0.5 for index in range(len(keys))],tuple(sorted(keys)),fontsize=20)\n",
    "plt.yticks([index+0.5 for index in range(len(regions))],tuple(regions),rotation = 0,fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{6: array([ 556.05766294,    0.        ,  169.00877381,    0.        ,\n",
       "          49.48651506,    0.        ,    0.        ,   12.39259625,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "         774.39904787,   25.472332  ,  119.76826475,    0.        ,\n",
       "         607.3529635 ,  283.6189575 ,   16.98977469, 1226.08996581,\n",
       "          47.69540744,  991.59886175,   46.79280856,   96.30331994,\n",
       "           0.        ,    0.        ,  425.92080687,    0.        ,\n",
       "          98.30664062,    0.        ,    0.        ,    0.        ,\n",
       "         118.21015169,  176.04172088,    0.        ,    0.        ,\n",
       "           0.        ,   29.37467575,    0.        ,   12.87283994,\n",
       "           0.        ,    5.81284569,    1.855324  ,    3.14221956,\n",
       "          58.0826645 ,    0.        ,    0.        ,    0.        ]),\n",
       " 7: array([  71.78417879,   48.82798497,  598.57883588,  503.51136421,\n",
       "         168.82359579,  177.19033856, 1212.40011776,   66.36890412,\n",
       "         141.41995868,  181.46294424,   67.7593725 ,  302.22163206,\n",
       "         231.08776856,   53.92282644,   41.74946147,    0.        ,\n",
       "         582.53995603,  303.91443679,  376.20591109, 4018.7378145 ,\n",
       "        1221.47730259, 2209.32648579,   31.49950162, 1218.23411921,\n",
       "         255.82242376,  174.80632265, 1224.77079871,   32.84030432,\n",
       "         584.78002571,  172.31432971,  114.05107926,   14.39057609,\n",
       "         847.23891491,  804.05886221,   14.56547371,   41.57380229,\n",
       "         102.86469135,  708.32854147,  697.86060559,  180.29965524,\n",
       "          71.31638829,  480.0459505 ,  368.30757029,  102.72404121,\n",
       "          12.73872735,    0.        ,    0.        ,    0.        ]),\n",
       " 1: array([3.00392332e+02, 1.24939993e+01, 1.43505402e+02, 3.22257622e+01,\n",
       "        5.03697048e+01, 0.00000000e+00, 6.76188066e+01, 5.92080303e+01,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.98358758e+00,\n",
       "        8.26753771e+02, 2.18792224e+00, 1.17009172e+02, 4.51645508e+01,\n",
       "        6.07354833e+02, 6.07373240e+01, 1.46732525e+02, 1.46658867e+03,\n",
       "        3.87557932e+02, 9.02116116e+02, 8.58518291e+01, 1.40035926e+02,\n",
       "        3.27277784e+01, 2.01964995e+02, 1.06227625e+03, 1.54189163e+02,\n",
       "        7.95962222e+02, 1.26167143e+02, 8.94378200e+01, 5.90534816e-01,\n",
       "        4.38140667e+02, 5.74297718e+02, 1.00845987e+02, 4.73497202e+01,\n",
       "        4.42396060e+02, 1.07591002e+03, 5.19158722e+02, 3.38750170e+02,\n",
       "        4.13465655e+02, 4.52626411e+02, 4.78174740e+02, 4.70155000e+02,\n",
       "        5.29631155e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
       " 10: array([0.00000000e+00, 0.00000000e+00, 2.19787933e+02, 2.39156317e+02,\n",
       "        1.23942568e+02, 1.82945602e+02, 1.63703461e+02, 1.16426445e+02,\n",
       "        1.48209305e+02, 9.80763855e+01, 0.00000000e+00, 2.56013794e+02,\n",
       "        3.25821747e+02, 0.00000000e+00, 1.24266472e+02, 0.00000000e+00,\n",
       "        2.29665138e+02, 8.67926294e+02, 4.93586578e+02, 2.80906311e+03,\n",
       "        1.05561200e+03, 8.88776918e+02, 4.43500564e+02, 2.10497885e+03,\n",
       "        3.62061405e+01, 0.00000000e+00, 1.09050961e+03, 4.56790867e+01,\n",
       "        3.05724098e+02, 1.41684955e+02, 5.64020157e+01, 0.00000000e+00,\n",
       "        3.83194099e+02, 2.66035937e-01, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.31351654e+02, 0.00000000e+00, 0.00000000e+00,\n",
       "        4.48296280e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
       " 9: array([3.24987973e+02, 0.00000000e+00, 4.84471635e+02, 1.18296150e+02,\n",
       "        6.35938088e+01, 5.03803108e+02, 4.94859212e+03, 6.53166041e+01,\n",
       "        7.24766719e+02, 1.12316044e+03, 1.34739052e+02, 4.35404307e+02,\n",
       "        1.13560015e+03, 0.00000000e+00, 4.19967378e+01, 0.00000000e+00,\n",
       "        3.90583478e+02, 2.01606134e+02, 1.27981303e+00, 1.98557156e+03,\n",
       "        2.86921038e+02, 6.84228813e+02, 8.35797308e+01, 8.03679890e+02,\n",
       "        3.72046193e+01, 0.00000000e+00, 9.38162559e+02, 0.00000000e+00,\n",
       "        9.50583243e+01, 1.96589663e+01, 3.03599648e+01, 1.41282852e+00,\n",
       "        1.34786959e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 2.30150841e+00, 3.92779120e+01, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
       " 5: array([ 808.48721369,  646.61375085,   85.51349875,   68.20015704,\n",
       "          38.43129716,   38.34398858,   79.2580644 ,  208.38804155,\n",
       "          40.246258  ,    0.        ,   15.38709827,   18.19764295,\n",
       "        2037.94225853,  650.28207675,  105.7476894 ,  480.12916149,\n",
       "         496.81567244,   73.474727  ,  147.36594653,  755.16188464,\n",
       "         374.95475431, 3190.3224812 ,  312.98063035,  509.67239925,\n",
       "         151.66311645,   25.38047651,  552.19992204,   71.49052956,\n",
       "          47.51958093,   57.22648482,    8.05148344,    7.77715878,\n",
       "         199.99811205,  303.5777846 ,   22.41744496,  140.09066051,\n",
       "          43.05476407,    0.        ,   11.63977051,   11.14880038,\n",
       "           0.        ,   55.04880149,   87.25336471,   21.21173131,\n",
       "          52.33394345,    0.        ,    0.        ,    0.        ]),\n",
       " 4: array([   0.        ,    0.        ,    0.        ,    0.        ,\n",
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       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,   29.65150014,    0.        ,    0.        ,\n",
       "           0.        ,  155.78933607,  882.31134907,   67.60992864,\n",
       "          31.12573243,  173.31487164,   85.31900136,    0.        ,\n",
       "           0.        , 2194.63996   ,  307.61124307,  662.49115857,\n",
       "         581.68031093,    0.        ,  650.74571007,  437.17302629,\n",
       "           0.        , 1507.91767629,  855.56149729,  567.10448343,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ]),\n",
       " 2: array([   0.        ,    0.        ,    0.        ,    0.        ,\n",
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       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
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       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,  294.78173829,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    8.66635457,\n",
       "          21.05305257,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "          24.87084086,  207.53508643, 1054.58572829,  262.62147743]),\n",
       " 3: array([  0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,   0.        ,   0.        ,   0.        ,\n",
       "          0.        ,  22.37447845, 140.79227509,  31.86533145,\n",
       "        216.20026191,  28.43448982,  91.74607709,   0.        ,\n",
       "          0.        , 316.81410209,  18.51541273,   0.        ,\n",
       "        157.00004436, 493.95987218, 432.58065518, 389.68853209,\n",
       "        121.27683327, 383.25220836,  78.39629245, 483.46962391,\n",
       "        729.46706327,   0.        ,   0.        ,   0.        ])}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterProjectLeft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: array([   83.06017112,     0.        ,   364.5946665 ,   147.93049769,\n",
       "          230.14273063,   244.68954087,  1038.76771931,    75.25666806,\n",
       "          136.05858231,    62.38235469,    20.9471245 ,   347.25048831,\n",
       "          770.01556012,     0.        ,   482.17174625,     0.        ,\n",
       "          457.62782156,   948.81034275,   693.06316369,  6622.14100256,\n",
       "        10301.76562356,  3494.44826713,  1003.39460519,  2541.07613662,\n",
       "          180.05488394,   350.81300356,  2334.96802519,     0.        ,\n",
       "          336.33306887,   133.38412569,    25.15821031,     0.        ,\n",
       "          649.72300144,    26.83559606,     0.        ,     0.        ,\n",
       "            0.        ,   231.30838012,   244.85386275,     0.        ,\n",
       "            0.        ,     0.        ,   246.56740569,    59.15791513,\n",
       "           20.44691656,     0.        ,     0.        ,     0.        ]),\n",
       " 6: array([3.47042563e+03, 1.03501301e+02, 1.07159355e+03, 8.84414958e+02,\n",
       "        5.98750326e+01, 7.55183426e+02, 1.63425753e+03, 3.78571129e+02,\n",
       "        4.20623844e+02, 1.56046781e+02, 1.03263750e+00, 3.88593620e+02,\n",
       "        3.70684057e+03, 5.51516859e+02, 1.14078633e+03, 0.00000000e+00,\n",
       "        8.33034611e+02, 1.56997615e+03, 2.50426288e+03, 5.53797363e+03,\n",
       "        6.72229895e+02, 5.01998408e+03, 2.60140413e+02, 1.06598605e+03,\n",
       "        3.82884359e+02, 4.07670899e+01, 2.12757956e+03, 0.00000000e+00,\n",
       "        2.84324673e+02, 2.30702982e+01, 0.00000000e+00, 2.14490650e+00,\n",
       "        1.27946314e+03, 1.10527716e+03, 0.00000000e+00, 5.41819498e+02,\n",
       "        4.55083813e-01, 2.25992900e+02, 5.29807972e+02, 1.00289545e+01,\n",
       "        1.72919458e+02, 5.17322693e+01, 8.99461273e+02, 2.61154007e+02,\n",
       "        1.35084137e+02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
       " 7: array([ 390.75890038,  149.83615247, 2388.716239  , 1149.70780059,\n",
       "         450.41492756, 1079.96890412, 5256.93061009,  178.75752891,\n",
       "        1152.29866385,  319.26296262,   30.74674321,  923.70274538,\n",
       "        1985.59957794,  145.60903571,  341.23246232,    8.44925106,\n",
       "        2659.62077456, 1201.39319571,  916.14189324, 6549.60182465,\n",
       "        1982.08353894, 2114.76803906,  359.47540171, 2564.53262059,\n",
       "          61.24590224,  304.23647891, 2412.1403675 ,  128.80348726,\n",
       "         573.73153462,  313.46758585,  172.29627706,    0.        ,\n",
       "        1449.78600003, 1293.23544671,   32.55556891,   84.95674356,\n",
       "         154.87917235,  754.29193159,  399.82967329,  235.40769556,\n",
       "         255.42500944,  376.19837503,  700.32801909,  310.55240721,\n",
       "          98.0779715 ,    0.        ,    0.        ,    0.        ]),\n",
       " 9: array([2.10743552e+02, 0.00000000e+00, 1.74012272e+03, 1.17714890e+03,\n",
       "        3.81578479e+02, 1.57132952e+03, 1.18067080e+04, 1.27851847e+02,\n",
       "        2.90798737e+03, 1.86149698e+03, 1.39139432e+02, 3.53940282e+03,\n",
       "        9.03052137e+02, 0.00000000e+00, 3.79855911e+02, 3.27188952e+01,\n",
       "        1.91021816e+03, 7.91006301e+02, 1.31372966e+02, 6.41654049e+03,\n",
       "        2.43686607e+03, 2.87970231e+03, 5.97848740e+02, 1.21243376e+03,\n",
       "        1.96670434e+02, 8.22160350e+01, 1.75904430e+03, 0.00000000e+00,\n",
       "        3.34331803e+01, 8.59533997e+01, 4.12235208e+01, 4.45425941e+00,\n",
       "        1.35344947e+02, 7.56832486e+01, 0.00000000e+00, 6.87940000e+01,\n",
       "        2.52804839e+01, 5.09716166e+01, 4.42054949e+01, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 7.74880979e+01, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
       " 1: array([ 782.32423079,   28.5604505 ,  329.95753558,  109.28630997,\n",
       "          71.22033834,  183.25850555,  555.28528226,  265.20433847,\n",
       "         535.09257589,  234.26941324,   31.79525974,  196.64025732,\n",
       "        1911.81728608,  100.07482189,  426.81561279,   38.96870824,\n",
       "         390.28553416,   59.22144882,  299.94093263, 3800.57128226,\n",
       "         805.87939803, 3780.15460497,  414.43869547, 1641.59639737,\n",
       "         264.69068366,  809.25837829, 5000.19841995,  436.25192239,\n",
       "        2738.36939837,  820.74812479,  909.83635997,   74.52774842,\n",
       "        1807.14759584, 3666.89589397,  280.46094729,  993.51947626,\n",
       "        1201.38240363, 1940.05819529, 2660.72929824,  518.43094032,\n",
       "         694.03082355, 1966.97600482, 3380.96804608, 1097.79616708,\n",
       "         481.38065718,    0.        ,    0.        ,    0.        ]),\n",
       " 5: array([2.26260878e+03, 8.53339512e+02, 9.06522588e+01, 2.29954598e+02,\n",
       "        1.75871983e+02, 2.29148744e+02, 3.23043734e+02, 2.84074640e+02,\n",
       "        1.94814971e+02, 1.11251021e+01, 9.50502569e+00, 2.02681609e+02,\n",
       "        6.05951696e+03, 1.56950780e+03, 5.39431977e+02, 5.95508818e+02,\n",
       "        4.71490637e+02, 3.70882892e+02, 5.94333373e+02, 2.07137684e+03,\n",
       "        6.57082896e+02, 3.66494272e+03, 4.42683653e+02, 1.18566082e+03,\n",
       "        1.20156217e+02, 0.00000000e+00, 9.30079722e+02, 1.09652044e+02,\n",
       "        4.61435892e+01, 3.52020552e+01, 1.32198929e+01, 1.28485167e+01,\n",
       "        1.41730443e+02, 2.34842707e+01, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.60636719e+01, 0.00000000e+00, 1.76638544e+01, 2.63697299e+01,\n",
       "        0.00000000e+00, 2.63852700e+01, 0.00000000e+00, 1.88756330e+01,\n",
       "        2.81369296e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]),\n",
       " 8: array([4920.16472883,  489.314621  ,  570.18439675, 3636.76540108,\n",
       "         197.31139208,  774.38772192,  820.275309  ,   76.64904767,\n",
       "         479.30763433,  147.31968892,    0.        , 1652.1668445 ,\n",
       "        2387.77074425,  568.879687  ,  996.08046983,    0.        ,\n",
       "         595.95429425, 1544.58572392,  626.19023633, 6245.99519858,\n",
       "         673.31244308, 1711.07084283,  209.78577808,  774.22686417,\n",
       "         110.11158225,   42.29624942, 1077.472635  ,   14.96353658,\n",
       "           0.        ,  162.69205217,   81.02431742,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,  133.74340817,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ]),\n",
       " 4: array([   0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,   33.199707  ,\n",
       "           0.        ,    0.        ,    0.        ,  219.37588936,\n",
       "           0.        , 1961.85748064, 1853.78861564,  119.56821986,\n",
       "           0.        ,   56.58959743,    9.13242829,    0.        ,\n",
       "          75.81006729, 4266.67366907,   12.745333  ,    0.        ,\n",
       "        1650.0431475 ,    0.        ,  936.30749836,  329.03607179,\n",
       "           0.        ,  802.41766386,  217.70969064, 1232.68681986,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ]),\n",
       " 3: array([   0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        ,    0.        ,    0.        ,    0.        ,\n",
       "           0.        , 3245.60541891, 2131.47030355,  116.01599118,\n",
       "         139.78705391,   35.16810745,    0.        ,    0.        ,\n",
       "          91.30692364, 7875.40229164, 1369.26813018, 3519.26074218,\n",
       "         616.28511182,  703.49746982,  716.31214182,  714.730699  ,\n",
       "           0.        , 1180.40552055, 3443.88945427,  234.71576418,\n",
       "        1583.86542291,  408.55141382,  243.69153945,    0.        ]),\n",
       " 2: array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 9.02921629e+00, 2.96111401e+03, 2.68175886e+00])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterProjectRight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "leftdf = iondata.getPropertiesDF(leftNeurons)\n",
    "projectregiondf= leftdf[leftdf.property.str.contains('projectregion',case=False)]\n",
    "projectregiondf.set_index(['property'],inplace=True)\n",
    "\n",
    "data =projectregiondf.to_numpy().astype(np.float16)\n",
    "data=(np.log2(data/10000.0+0.25))\n",
    "fig,ax = plt.subplots(figsize=(7,13))\n",
    "\n",
    "sns.heatmap(data)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 ('neuronVis')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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